1. Field of the Invention
The present invention relates generally to the field of early detection and diagnosis of incipient machine failure or process upset. More particularly, the invention is directed to model-based monitoring of processes and machines, and experience-based diagnostics.
2. Brief Description of the Related Art
A variety of new and advanced techniques have emerged in industrial process control, machine control, system surveillance, and condition based monitoring to address drawbacks of traditional sensor-threshold-based control and alarms. The traditional techniques did little more than provide responses to gross changes in individual metrics of a process or machine, often failing to provide adequate warning to prevent unexpected shutdowns, equipment damage, loss of product quality or catastrophic safety hazards.
According to one branch of the new techniques, empirical models of the monitored process or machine are used in failure detection and in control. Such models effectively leverage an aggregate view of surveillance sensor data to achieve much earlier incipient failure detection and finer process control. By modeling the many sensors on a process or machine simultaneously and in view of one another, the surveillance system can provide more information about how each sensor (and its measured parameter) ought to behave. Additionally, these approaches have the advantage that no additional instrumentation is typically needed, and sensors in place on the process or machine can be used.
An example of such an empirical surveillance system is described in U.S. Pat. No. 5,764,509 to Gross et al., the teachings of which are incorporated herein by reference. Therein is described an empirical model using a similarity operator against a reference library of known states of the monitored process, and an estimation engine for generating estimates of current process states based on the similarity operation, coupled with a sensitive statistical hypothesis test to determine if the current process state is a normal or abnormal state. The role of the similarity operator in the above empirical surveillance system is to determine a metric of the similarity of a current set of sensor readings to any of the snapshots of sensor readings contained in the reference library. The similarity metric thusly rendered is used to generate an estimate of what the sensor readings ought to be, from a weighted composite of the reference library snapshots. The estimate can then be compared to the current readings for monitoring differences indicating incipient process upset, sensor failure or the like. Other empirical model-based monitoring systems known in the art employ neural networks to model the process or machine being monitored.
Early detection of sensor failure, process upset or machine fault are afforded in such monitoring systems by sensitive statistical tests such as the sequential probability ratio test, also described in the aforementioned patent to Gross et al. The result of such a test when applied to the residual of the difference of the actual sensor signal and estimated sensor signal, is a decision as to whether the actual and estimate signals are the same or different, with user-selectable statistical confidence. While this is useful information in itself, directing thinly stretched maintenance resources only to those process locations or machine subcomponents that evidence a change from normal, there is a need to advance monitoring to a diagnostic result, and thereby provide a likely failure mode, rather than just an alert that the signal is not behaving as normal. Coupling a sensitive early detection statistical test with an easy-to-build empirical model and providing not only early warning, but a diagnostic indication of what is the likely cause of a change, comprises an enormously valuable monitoring or control system, and is much sought after in a variety of industries currently.
Due to the inherent complexity of many processes and machines, the task of diagnosing a fault is very difficult. A great deal of effort has been spent on developing diagnostic systems. One approach to diagnosis has been to employ the use of an expert system that is a rule based system for analyzing process or machine parameters according to rules describing the dynamics of the monitored or controlled system developed by an expert. An expert system requires an intense learning process by a human expert to understand the system and to codify his knowledge into a set of rules. Thus, expert system development takes a large amount of time and resources. An expert system is not responsive to frequent design changes to a process or machine. A change in design changes the rules, which requires the expert to determine the new rules and to redesign the system.
What is needed is a diagnostic approach that can be combined with model-based monitoring and control of a process or machine, wherein an expert is not required to spend months developing rules to be implemented in software for diagnosing machine or process fault. A diagnostic system that could be built on the domain knowledge of the industrial user of the monitoring or control system would be ideal. Furthermore, a diagnostic approach is needed that is easily adapted to changing uses of a machine, or changing parameters of a process, as well as design changes to both.
What is further needed is a way to match precursors of impending failure to past patterns of precursors to known failures rapidly, accurately and without significant human expert time and effort.